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Analysis of mobility data to study and optimise public transports
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Analysis of mobility data to study and optimise public transports
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Academic year 2013/2014
- Course ID
- SEM-AMBSOPT
- Teaching period
- Seminario
- Type
- Seminario
- Course disciplinary sector (SSD)
- INF/01 - informatica
- Delivery
- Tradizionale
- Language
- Inglese
- Attendance
- Facoltativa
- Type of examination
- Non prevista
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Sommario del corso
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Program
As the world becomes more urbanized, cities have evolved into one of our most consummate and complex artifacts. As a result, urban transportation is facing a grand challenge that is often called sustainable mobility: devising more efficient and adaptive urban transportation systems necessary to accommodate urban dynamics while preserving and restoring the environment. The general consensus is that congestion reduction is instead better addressed through Intelligent Transportation Systems (ITS) that leverage sensor networks, communications and computing technologies to manage existing infrastructure and transportation systems more efficiently. More recently, our growing reliance on smartphones and other pervasive technologies is producing a wealth of digital information extremely valuable to ITS systems because of its unprecedented level of spatio-temporal details about many aspects of our daily lives and in particular our travelling patterns. It also provides a medium through which it is possible to reach-out to the travellers and engage them in adopting the most efficient means of transport. In particular, in this seminar we present the analysis of mobile phone traces to optimise the public transports in developing countries.
Suggested readings and bibliography
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Class schedule
Days Time Classroom Venerdì 10:00 - 12:00 Sala Seminari Dipartimento di Informatica Lessons: dal 06/12/2013 to 06/12/2013
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Note
The seminar will be held by
Dr. Fabio Pinelli, IBM Researcher - Ireland
SHORT BIO: Fabio Pinelli is currently a research scientist at Smarter City Technology Center at IBM research -- Ireland where he investigates new methods to extract useful knowledge from urban related data with special focus on Intelligent Transportation systems. He earned his PhD in Information Engineering from the University of Pisa where he studied new approaches for the analysis of trajectories of moving objects. During this period, he was also visiting student at Senseable City Lab -- M.I.T. His main research interests include spatio-temporal data mining, pervasive computing, intelligent transportation systems and urban dynamics.
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